In ML, kernel methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]} are a class of categorization algorithms. Used within a wide range of methods and algorithms, they include the support vector machine (SVM) [7]}, [8]}, [9]}, kernel operators with principal components analysis (PCA) [10]}, spectral clustering [11]}, canonical correlation analysis [12]}, linear adaptive filters [13]}, and ridge regression [14]}. Indeed, kernel methods are proving to assist also in deep neural networks, for which there are many recently published works [15]}, [16]}. There exist vast prospects of kernel methods in ML due to the non-linear nature of the underlying data. Within application settings, the data are usually non-separable, for which the requirement of the kernel then becomes transformations (of the data) into higher dimensions where it may be (linearly) separable as can be seen in Figure REF .
One of the well-known
kernel functions in ML, the Radial Basis function (RBF),
is defined by \(K(x,x^{\prime }) = \exp \left[-|x-x^{\prime }|^{2}/2\sigma ^{2}\right]\) 
where \(x\)  and \(x^{\prime }\)  are two sample elements, and \(\sigma \)  controls the decision boundary [17]}, [18]}, [19]}. It is worthwhile to mention that the RBF can be understood as an inner product of the linear coherent state, see [20]}, which is defined as an eigenstate of the annihilation operator of a harmonic oscillator.
The idea of using a quantum mechanics formalism in kernel methods was suggested by Schuld and Killoran, who introduced squeezed kernels in feature spaces [21]}. In fact, they defined the feature space as a set of squeezed states so that the kernel is obtained by inner products of squeezed states [22]}, [23]}.
